Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors

ABSTRACT

Method, system and computer program product for providing real time detection of analyte sensor sensitivity decline is continuous glucose monitoring systems are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/866,384, filed Jan. 9, 2018, which is a continuation of U.S.patent application Ser. No. 14/266,612, filed Apr. 30, 2014, now U.S.Pat. No. 9,882,660, which is a continuation of U.S. patent applicationSer. No. 13/418,305, filed Mar. 12, 2012, now U.S. Pat. No. 8,718,958,which is a continuation of U.S. patent application Ser. No. 11/925,689,filed Oct. 26, 2007, now U.S. Pat. No. 8,135,548, which claims priorityto U.S. Provisional Application No. 60/854,566, filed Oct. 26, 2006, allof which are incorporated herein by reference in their entireties forall purposes.

BACKGROUND

Analyte, e.g., glucose monitoring systems including continuous anddiscrete monitoring systems generally include a small, lightweightbattery powered and microprocessor controlled system which is configuredto detect signals proportional to the corresponding measured glucoselevels using an electrometer, and RF signals to transmit the collecteddata. One aspect of certain analyte monitoring systems include atranscutaneous or subcutaneous analyte sensor configuration which is,for example, partially mounted on the skin of a subject whose analytelevel is to be monitored. The sensor cell may use a two orthree-electrode (work, reference and counter electrodes) configurationdriven by a controlled potential (potentiostat) analog circuit connectedthrough a contact system.

The analyte sensor may be configured so that a portion thereof is placedunder the skin of the patient so as to detect the analyte levels of thepatient, and another portion of segment of the analyte sensor that is incommunication with the transmitter unit. The transmitter unit isconfigured to transmit the analyte levels detected by the sensor over awireless communication link such as an RF (radio frequency)communication link to a receiver/monitor unit. The receiver/monitor unitperforms data analysis, among others on the received analyte levels togenerate information pertaining to the monitored analyte levels.

In view of the foregoing, it would be desirable to have an accurateassessment of the glucose level fluctuations, and in particular, thedetection of analyte sensor signal dropouts of sensor sensitivityreferred to as Early Signal Attenuation (ESA).

SUMMARY OF THE DISCLOSURE

In one embodiment, method, system and computer program product forreceiving a plurality of analyte sensor related signals, determining aprobability of signal attenuation associated with the received pluralityof analyte sensor related signals, verifying the presence of signalattenuation when the determined probability exceeds a predeterminedthreshold level, and generating a first output signal associated withthe verification of the presence of signal attenuation, are disclosed.

These and other objects, features and advantages of the presentdisclosure will become more fully apparent from the following detaileddescription of the embodiments, the appended claims and the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a data monitoring and managementsystem for practicing one or more embodiments of the present disclosure;

FIG. 2 is a block diagram of the transmitter unit of the data monitoringand management system shown in FIG. 1 in accordance with one embodimentof the present disclosure;

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure;

FIGS. 4A-4B illustrate a perspective view and a cross sectional view,respectively of an analyte sensor in accordance with one embodiment ofthe present disclosure;

FIG. 5 is a block diagram illustrating real time early signalattenuation (ESA) in one embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating an overall ESA detection routine inaccordance with one embodiment of the present disclosure;

FIG. 7 is a flowchart illustrating real-time detection of sensor currentabnormalities described in conjunction with module 1 of FIG. 5 inaccordance with one embodiment of the present disclosure;

FIG. 8 is a flowchart illustrating verification routine of module 2 inFIG. 5 to confirm or reject the output of module 1 in accordance withone embodiment of the present disclosure;

FIG. 9 illustrates a real time current signal characteristics evaluationapproach based on a sliding window process of module 1 in FIG. 5 inaccordance with one embodiment of the present disclosure;

FIG. 10 illustrates bootstrap estimation of coefficients for module 1 ofFIG. 5 in accordance with one embodiment of the present disclosure;

FIG. 11 illustrates Gaussian kernel estimation of the normalizedsensitivity density of module 2 of FIG. 5 in accordance with oneembodiment of the present disclosure; and

FIG. 12 illustrates output curve of the first module compared with theoutput curve of the combined first and second modules of FIG. 5, basedon a predetermined test data set and detection threshold of FIG. 7 inaccordance with embodiment of the present disclosure.

DETAILED DESCRIPTION

As described in further detail below, in accordance with the variousembodiments of the present disclosure, there is provided method, systemand computer program product for real time detection of analyte sensorsensitivity decline in data processing and control systems including,for example, analyte monitoring systems. In particular, within the scopeof the present disclosure, there are provided method, system andcomputer program product for the detection of episodes of low sensorsensitivity that may cause clinically significant sensor related errors,including, for example, early sensor attenuation (ESA) represented bysensor sensitivity (defined as the ratio between the analyte sensorcurrent level and the blood glucose level) decline which may existduring the initial 12-24 hours of the sensor life, or during night timeuse of the analyte sensor (“night time dropouts”).

FIG. 1 illustrates a data monitoring and management system such as, forexample, analyte (e.g., glucose) monitoring system 100 in accordancewith one embodiment of the present disclosure. The subject disclosure isfurther described primarily with respect to a glucose monitoring systemfor convenience and such description is in no way intended to limit thescope of the disclosure. It is to be understood that the analytemonitoring system may be configured to monitor a variety of analytes,e.g., lactate, and the like.

Analytes that may be monitored include, for example, acetyl choline,amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase(e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growthhormones, hormones, ketones, lactate, peroxide, prostate-specificantigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.The concentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, may also be monitored.

The analyte monitoring system 100 includes a sensor 101, a transmitterunit 102 coupled to the sensor 101, and a primary receiver unit 104which is configured to communicate with the transmitter unit 102 via acommunication link 103. The primary receiver unit 104 may be furtherconfigured to transmit data to a data processing terminal 105 forevaluating the data received by the primary receiver unit 104. Moreover,the data processing terminal 105 in one embodiment may be configured toreceive data directly from the transmitter unit 102 via a communicationlink which may optionally be configured for bi-directionalcommunication.

Also shown in FIG. 1 is a secondary receiver unit 106 which isoperatively coupled to the communication link 103 and configured toreceive data transmitted from the transmitter unit 102. Moreover, asshown in the Figure, the secondary receiver unit 106 is configured tocommunicate with the primary receiver unit 104 as well as the dataprocessing terminal 105. Indeed, the secondary receiver unit 106 may beconfigured for bi-directional wireless communication with each of theprimary receiver unit 104 and the data processing terminal 105. Asdiscussed in further detail below, in one embodiment of the presentdisclosure, the secondary receiver unit 106 may be configured to includea limited number of functions and features as compared with the primaryreceiver unit 104. As such, the secondary receiver unit 106 may beconfigured substantially in a smaller compact housing or embodied in adevice such as a wrist watch, for example. Alternatively, the secondaryreceiver unit 106 may be configured with the same or substantiallysimilar functionality as the primary receiver unit 104, and may beconfigured to be used in conjunction with a docking cradle unit forplacement by bedside, for night time monitoring, and/or bi-directionalcommunication device.

Only one sensor 101, transmitter unit 102, communication link 103, anddata processing terminal 105 are shown in the embodiment of the analytemonitoring system 100 illustrated in FIG. 1. However, it will beappreciated by one of ordinary skill in the art that the analytemonitoring system 100 may include one or more sensor 101, transmitterunit 102, communication link 103, and data processing terminal 105.Moreover, within the scope of the present disclosure, the analytemonitoring system 100 may be a continuous monitoring system, orsemi-continuous, or a discrete monitoring system. In a multi-componentenvironment, each device is configured to be uniquely identified by eachof the other devices in the system so that communication conflict isreadily resolved between the various components within the analytemonitoring system 100.

In one embodiment of the present disclosure, the sensor 101 isphysically positioned in or on the body of a user whose analyte level isbeing monitored. The sensor 101 may be configured to continuously samplethe analyte level of the user and convert the sampled analyte level intoa corresponding data signal for transmission by the transmitter unit102. In one embodiment, the transmitter unit 102 is coupled to thesensor 101 so that both devices are positioned on the user's body, withat least a portion of the analyte sensor 101 positioned transcutaneouslyunder the skin layer of the user. The transmitter unit 102 performs dataprocessing such as filtering and encoding on data signals, each of whichcorresponds to a sampled analyte level of the user, for transmission tothe primary receiver unit 104 via the communication link 103.

In one embodiment, the analyte monitoring system 100 is configured as aone-way RF communication path from the transmitter unit 102 to theprimary receiver unit 104. In such embodiment, the transmitter unit 102transmits the sampled data signals received from the sensor 101 withoutacknowledgement from the primary receiver unit 104 that the transmittedsampled data signals have been received. For example, the transmitterunit 102 may be configured to transmit the encoded sampled data signalsat a fixed rate (e.g., at one minute intervals) after the completion ofthe initial power on procedure. Likewise, the primary receiver unit 104may be configured to detect such transmitted encoded sampled datasignals at predetermined time intervals. Alternatively, the analytemonitoring system 100 may be configured with a bi-directional RF (orotherwise) communication between the transmitter unit 102 and theprimary receiver unit 104.

Additionally, in one aspect, the primary receiver unit 104 may includetwo sections. The first section is an analog interface section that isconfigured to communicate with the transmitter unit 102 via thecommunication link 103. In one embodiment, the analog interface sectionmay include an RF receiver and an antenna for receiving and amplifyingthe data signals from the transmitter unit 102, which are thereafter,demodulated with a local oscillator and filtered through a band-passfilter. The second section of the primary receiver unit 104 is a dataprocessing section which is configured to process the data signalsreceived from the transmitter unit 102 such as by performing datadecoding, error detection and correction, data clock generation, anddata bit recovery.

In operation, upon completing the power-on procedure, the primaryreceiver unit 104 is configured to detect the presence of thetransmitter unit 102 within its range based on, for example, thestrength of the detected data signals received from the transmitter unit102 or predetermined transmitter identification information. Uponsuccessful synchronization with the corresponding transmitter unit 102,the primary receiver unit 104 is configured to begin receiving from thetransmitter unit 102 data signals corresponding to the user's detectedanalyte level. More specifically, the primary receiver unit 104 in oneembodiment is configured to perform synchronized time hopping with thecorresponding synchronized transmitter unit 102 via the communicationlink 103 to obtain the user's detected analyte level.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs)), and the like, each ofwhich may be configured for data communication with the receiver via awired or a wireless connection. Additionally, the data processingterminal 105 may further be connected to a data network (not shown) forstoring, retrieving and updating data corresponding to the detectedanalyte level of the user.

Within the scope of the present disclosure, the data processing terminal105 may include an infusion device such as an insulin infusion pump orthe like, which may be configured to administer insulin to patients, andwhich may be configured to communicate with the receiver unit 104 forreceiving, among others, the measured analyte level. Alternatively, thereceiver unit 104 may be configured to integrate an infusion devicetherein so that the receiver unit 104 is configured to administerinsulin therapy to patients, for example, for administering andmodifying basal profiles, as well as for determining appropriate bolusesfor administration based on, among others, the detected analyte levelsreceived from the transmitter unit 102.

Additionally, the transmitter unit 102, the primary receiver unit 104and the data processing terminal 105 may each be configured forbi-directional wireless communication such that each of the transmitterunit 102, the primary receiver unit 104 and the data processing terminal105 may be configured to communicate (that is, transmit data to andreceive data from) with each other via a wireless communication link.More specifically, the data processing terminal 105 may in oneembodiment be configured to receive data directly from the transmitterunit 102 via a communication link, where the communication link, asdescribed above, may be configured for bi-directional communication.

In this embodiment, the data processing terminal 105 which may includean insulin pump, may be configured to receive the analyte signals fromthe transmitter unit 102, and thus, incorporate the functions of thereceiver unit 104 including data processing for managing the patient'sinsulin therapy and analyte monitoring. In one embodiment, thecommunication link 103 may include one or more of an RF communicationprotocol, an infrared communication protocol, a Bluetooth® enabledcommunication protocol, an 802.11x wireless communication protocol, oran equivalent wireless communication protocol which would allow secure,wireless communication of several units (for example, per HIPAArequirements) while avoiding potential data collision and interference.

FIG. 2 is a block diagram of the transmitter of the data monitoring anddetection system shown in FIG. 1 in accordance with one embodiment ofthe present disclosure. Referring to the Figure, the transmitter unit102 in one embodiment includes an analog interface 201 configured tocommunicate with the sensor 101 (FIG. 1), a user input 202, and atemperature measurement section 203, each of which is operativelycoupled to a transmitter processor 204 such as a central processing unit(CPU).

Further shown in FIG. 2 are a transmitter serial communication section205 and an RF transmitter 206, each of which is also operatively coupledto the transmitter processor 204. Moreover, a power supply 207 such as abattery is also provided in the transmitter unit 102 to provide thenecessary power for the transmitter unit 102. Additionally, as can beseen from the Figure, a clock 208 is provided to, among others, supplyreal time information to the transmitter processor 204.

As can be seen from FIG. 2, the sensor 101 (FIG. 1) is provided fourcontacts, three of which are electrodes—work electrode (W) 210, guardcontact (G) 211, reference electrode (R) 212, and counter electrode (C)213, each operatively coupled to the analog interface 201 of thetransmitter unit 102. In one embodiment, each of the work electrode (W)210, guard contact (G) 211, reference electrode (R) 212, and counterelectrode (C) 213 may be made using a conductive material that is eitherprinted or etched, for example, such as carbon which may be printed, ormetal foil (e.g., gold) which may be etched, or alternatively providedon a substrate material using laser or photolithography.

In one embodiment, a unidirectional input path is established from thesensor 101 (FIG. 1) and/or manufacturing and testing equipment to theanalog interface 201 of the transmitter unit 102, while a unidirectionaloutput is established from the output of the RF transmitter 206 of thetransmitter unit 102 for transmission to the primary receiver unit 104.In this manner, a data path is shown in FIG. 2 between theaforementioned unidirectional input and output via a dedicated link 209from the analog interface 201 to serial communication section 205,thereafter to the processor 204, and then to the RF transmitter 206. Assuch, in one embodiment, via the data path described above, thetransmitter unit 102 is configured to transmit to the primary receiverunit 104 (FIG. 1), via the communication link 103 (FIG. 1), processedand encoded data signals received from the sensor 101 (FIG. 1).Additionally, the unidirectional communication data path between theanalog interface 201 and the RF transmitter 206 discussed above allowsfor the configuration of the transmitter unit 102 for operation uponcompletion of the manufacturing process as well as for directcommunication for diagnostic and testing purposes.

As discussed above, the transmitter processor 204 is configured totransmit control signals to the various sections of the transmitter unit102 during the operation of the transmitter unit 102. In one embodiment,the transmitter processor 204 also includes a memory (not shown) forstoring data such as the identification information for the transmitterunit 102, as well as the data signals received from the sensor 101. Thestored information may be retrieved and processed for transmission tothe primary receiver unit 104 under the control of the transmitterprocessor 204. Furthermore, the power supply 207 may include acommercially available battery.

The transmitter unit 102 is also configured such that the power supplysection 207 is capable of providing power to the transmitter for aminimum of about three months of continuous operation after having beenstored for about eighteen months in a low-power (non-operating) mode. Inone embodiment, this may be achieved by the transmitter processor 204operating in low power modes in the non-operating state, for example,drawing no more than approximately 1 μA of current. Indeed, in oneembodiment, the final step during the manufacturing process of thetransmitter unit 102 may place the transmitter unit 102 in the lowerpower, non-operating state (i.e., post-manufacture sleep mode). In thismanner, the shelf life of the transmitter unit 102 may be significantlyimproved. Moreover, as shown in FIG. 2, while the power supply unit 207is shown as coupled to the processor 204, and as such, the processor 204is configured to provide control of the power supply unit 207, it shouldbe noted that within the scope of the present disclosure, the powersupply unit 207 is configured to provide the necessary power to each ofthe components of the transmitter unit 102 shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of thetransmitter unit 102 in one embodiment may include a rechargeablebattery unit that may be recharged by a separate power supply rechargingunit (for example, provided in the receiver unit 104) so that thetransmitter unit 102 may be powered for a longer period of usage time.Moreover, in one embodiment, the transmitter unit 102 may be configuredwithout a battery in the power supply section 207, in which case thetransmitter unit 102 may be configured to receive power from an externalpower supply source (for example, a battery) as discussed in furtherdetail below.

Referring yet again to FIG. 2, the temperature measurement section 203of the transmitter unit 102 is configured to monitor the temperature ofthe skin near the sensor insertion site. The temperature reading is usedto adjust the analyte readings obtained from the analog interface 201.The RF transmitter 206 of the transmitter unit 102 may be configured foroperation in the frequency band of 315 MHz to 322 MHz, for example, inthe United States. Further, in one embodiment, the RF transmitter 206 isconfigured to modulate the carrier frequency by performing FrequencyShift Keying and Manchester encoding. In one embodiment, the datatransmission rate is 19,200 symbols per second, with a minimumtransmission range for communication with the primary receiver unit 104.

Referring yet again to FIG. 2, also shown is a leak detection circuit214 coupled to the guard contact (G) 211 and the processor 204 in thetransmitter unit 102 of the data monitoring and management system 100.The leak detection circuit 214 in accordance with one embodiment of thepresent disclosure may be configured to detect leakage current in thesensor 101 to determine whether the measured sensor data is corrupt orwhether the measured data from the sensor 101 is accurate.

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure. Referring to FIG. 3, the primaryreceiver unit 104 includes a blood glucose test strip interface 301, anRF receiver 302, an input 303, a temperature monitor section 304, and aclock 305, each of which is operatively coupled to a receiver processor307. As can be further seen from the Figure, the primary receiver unit104 also includes a power supply 306 operatively coupled to a powerconversion and monitoring section 308. Further, the power conversion andmonitoring section 308 is also coupled to the receiver processor 307.Moreover, also shown are a receiver serial communication section 309,and an output 310, each operatively coupled to the receiver processor307.

In one embodiment, the test strip interface 301 includes a glucose leveltesting portion to receive a manual insertion of a glucose test strip,and thereby determine and display the glucose level of the test strip onthe output 310 of the primary receiver unit 104. This manual testing ofglucose can be used to calibrate sensor 101. The RF receiver 302 isconfigured to communicate, via the communication link 103 (FIG. 1) withthe RF transmitter 206 of the transmitter unit 102, to receive encodeddata signals from the transmitter unit 102 for, among others, signalmixing, demodulation, and other data processing. The input 303 of theprimary receiver unit 104 is configured to allow the user to enterinformation into the primary receiver unit 104 as needed. In one aspect,the input 303 may include one or more keys of a keypad, atouch-sensitive screen, or a voice-activated input command unit. Thetemperature monitor section 304 is configured to provide temperatureinformation of the primary receiver unit 104 to the receiver processor307, while the clock 305 provides, among others, real time informationto the receiver processor 307.

Each of the various components of the primary receiver unit 104 shown inFIG. 3 is powered by the power supply 306 which, in one embodiment,includes a battery. Furthermore, the power conversion and monitoringsection 308 is configured to monitor the power usage by the variouscomponents in the primary receiver unit 104 for effective powermanagement and to alert the user, for example, in the event of powerusage which renders the primary receiver unit 104 in sub-optimaloperating conditions. An example of such sub-optimal operating conditionmay include, for example, operating the vibration output mode (asdiscussed below) for a period of time thus substantially draining thepower supply 306 while the processor 307 (thus, the primary receiverunit 104) is turned on. Moreover, the power conversion and monitoringsection 308 may additionally be configured to include a reverse polarityprotection circuit such as a field effect transistor (FET) configured asa battery activated switch.

The serial communication section 309 in the primary receiver unit 104 isconfigured to provide a bi-directional communication path from thetesting and/or manufacturing equipment for, among others,initialization, testing, and configuration of the primary receiver unit104. Serial communication section 309 can also be used to upload data toa computer, such as time-stamped blood glucose data. The communicationlink with an external device (not shown) can be made, for example, bycable, infrared (IR) or RF link. The output 310 of the primary receiverunit 104 is configured to provide, among others, a graphical userinterface (GUI) such as a liquid crystal display (LCD) for displayinginformation. Additionally, the output 310 may also include an integratedspeaker for outputting audible signals as well as to provide vibrationoutput as commonly found in handheld electronic devices, such as mobiletelephones presently available. In a further embodiment, the primaryreceiver unit 104 also includes an electro-luminescent lamp configuredto provide backlighting to the output 310 for output visual display indark ambient surroundings.

Referring back to FIG. 3, the primary receiver unit 104 in oneembodiment may also include a storage section such as a programmable,non-volatile memory device as part of the processor 307, or providedseparately in the primary receiver unit 104, operatively coupled to theprocessor 307. The processor 307 is further configured to performManchester decoding as well as error detection and correction upon theencoded data signals received from the transmitter unit 102 via thecommunication link 103.

In a further embodiment, the one or more of the transmitter unit 102,the primary receiver unit 104, secondary receiver unit 106, or the dataprocessing terminal/infusion section 105 may be configured to receivethe blood glucose value wirelessly over a communication link from, forexample, a glucose meter. In still a further embodiment, the user orpatient manipulating or using the analyte monitoring system 100 (FIG. 1)may manually input the blood glucose value using, for example, a userinterface (for example, a keyboard, keypad, and the like) incorporatedin the one or more of the transmitter unit 102, the primary receiverunit 104, secondary receiver unit 106, or the data processingterminal/infusion section 105.

Additional detailed description of the continuous analyte monitoringsystem, its various components including the functional descriptions ofthe transmitter are provided in U.S. Pat. No. 6,175,752 issued Jan. 16,2001 entitled “Analyte Monitoring Device and Methods of Use”, and inapplication Ser. No. 10/745,878 filed Dec. 26, 2003 entitled “ContinuousGlucose Monitoring System and Methods of Use”, each assigned to AbbottDiabetes Care Inc., of Alameda, Calif.

FIGS. 4A-4B illustrate a perspective view and a cross sectional view,respectively of an analyte sensor in accordance with one embodiment ofthe present disclosure. Referring to FIG. 4A, a perspective view of asensor 400, the major portion of which is above the surface of the skin410, with an insertion tip 430 penetrating through the skin and into thesubcutaneous space 420 in contact with the user's biofluid such asinterstitial fluid. Contact portions of a working electrode 401, areference electrode 402, and a counter electrode 403 can be seen on theportion of the sensor 400 situated above the skin surface 410. Workingelectrode 401, a reference electrode 402, and a counter electrode 403can be seen at the end of the insertion tip 430.

Referring now to FIG. 4B, a cross sectional view of the sensor 400 inone embodiment is shown. In particular, it can be seen that the variouselectrodes of the sensor 400 as well as the substrate and the dielectriclayers are provided in a stacked or layered configuration orconstruction. For example, as shown in FIG. 4B, in one aspect, thesensor 400 (such as the sensor 101 FIG. 1), includes a substrate layer404, and a first conducting layer 401 such as a carbon trace disposed onat least a portion of the substrate layer 404, and which may comprisethe working electrode. Also shown disposed on at least a portion of thefirst conducting layer 401 is a sensing layer 408.

Referring back to FIG. 4B, a first insulation layer such as a firstdielectric layer 405 is disposed or stacked on at least a portion of thefirst conducting layer 401, and further, a second conducting layer 409such as another carbon trace may be disposed or stacked on top of atleast a portion of the first insulation layer (or dielectric layer) 405.As shown in FIG. 4B, the second conducting layer 409 may comprise thereference electrode 402, and in one aspect, may include a layer ofsilver/silver chloride (Ag/AgCl).

Referring still again to FIG. 4B, a second insulation layer 406 such asa dielectric layer in one embodiment may be disposed or stacked on atleast a portion of the second conducting layer 409. Further, a thirdconducting layer 403 which may include carbon trace and that maycomprise the counter electrode may in one embodiment be disposed on atleast a portion of the second insulation layer 406. Finally, a thirdinsulation layer 407 is disposed or stacked on at least a portion of thethird conducting layer 403. In this manner, the sensor 400 may beconfigured in a stacked or layered construction or configuration suchthat at least a portion of each of the conducting layers is separated bya respective insulation layer (for example, a dielectric layer).

Additionally, within the scope of the present disclosure, some or all ofthe electrodes 401, 402, 403 may be provided on the same side of thesubstrate 404 in a stacked construction as described above, oralternatively, may be provided in a co-planar manner such that eachelectrode is disposed on the same plane on the substrate 404, however,with a dielectric material or insulation material disposed between theconducting layers/electrodes. Furthermore, in still another aspect ofthe present disclosure, the one or more conducting layers such as theelectrodes 401, 402, 403 may be disposed on opposing sides of thesubstrate 404.

FIG. 5 is a block diagram illustrating real time early signalattenuation (ESA) in one embodiment of the present disclosure. Referringto FIG. 5, in one embodiment, the overall sensitivity decline detector500 includes a first module 510 configured to perform an estimation ofthe probability of sensitivity decline based on a window of analytesensor measurements to determine whether a finger stick measurement ofblood glucose level is necessary. Based on the estimated probability ofthe sensitivity decline performed by the first module 510, when it isdetermined that the finger stick measurement of the blood glucose levelis necessary, as shown in FIG. 5, in one aspect of the presentdisclosure, the second module 520 uses the measured blood glucose valueto verify or otherwise confirm or reject the estimated probability ofthe sensitivity decline performed by the first module 510. In oneaspect, the second module 520 may be configured to confirm or reject theresults of the first module 510 (e.g., the estimated probability ofsensitivity decline) based upon a statistical determination.

That is, in one aspect, the first module 510 of the sensitivity declinedetector 500 of FIG. 5 may be configured to estimate the probability ofthe analyte sensor sensitivity decline based on an analysis of a windowof sensor values (for example, current signals from the analyte sensorfor a predetermined time period). More specifically, the first module510 may be configured to estimate the probability of the sensorsensitivity decline based on a sliding window extractor of sensorcurrent signal characteristics, a model based estimation of theprobability of sensitivity decline based on the determined or retrievedsensor current signal characteristics, and/or a comparison of theestimated probability to a predetermined threshold value T.

Referring back to FIG. 5, the logistic estimator 511 of the first module510 may be configured in one embodiment to retrieve or extract a slidingwindow of sensor current signal characteristics, and to perform theestimation of the probability of the sensitivity decline based on thesensor current signal characteristics, and to compare the estimatedprobability of the sensitivity decline to a predetermined threshold T todetermine, whether verification of the estimated sensitivity decline isdesired, or whether it can be confirmed that ESA or night time drop outsis not detected based on the estimated probability of the sensitivitydecline.

Referring again to FIG. 5, as shown, when it is determined thatconfirmation or verification of the estimated probability of thesensitivity decline is desired (based on, for example, when theestimated probability exceeds the predetermined threshold value Tdetermined in the first module 510), hypothesis analysis module 521 ofthe second module 520 in the sensitivity decline detector 500 in oneembodiment receives the capillary blood measurement from a blood glucosemeasurement device such as a blood glucose meter including FreesStyle®Lite, Freestyle Flash®, FreeStyle Freedom®, or Precision Xtra™ bloodglucose meters commercially available from Abbott Diabetes Care Inc., ofAlameda, Calif. In one aspect, based on the received capillary bloodglucose measurement and the analyte sensor current characteristics orvalues, the estimated probability of the sensor sensitivity decline maybe confirmed or rejected, thus confirming the presence of ESA or nighttime drop out (in the event the corresponding data point is associatedwith night time sensor current value), or alternatively, confirming thatthe ESA or night time drop out is not present.

In the manner described, in one aspect of the present disclosure, thereis provided a real time detection routine based on sensor current signalcharacteristics, where the detector 500 (FIG. 5) includes a first module510 configured in one embodiment to perform the detection and estimationof the probability of the sensor sensitivity decline, and a secondmodule 520 configured in one aspect to verify the presence or absence ofESA or night time dropouts based on the probability estimationsdetermined by the first module 510. Accordingly, in one aspect of thepresent disclosure, ESA episodes or night time declines or dropouts maybe accurately detected while minimizing the potential for false alarmsor false negatives.

Referring again to FIG. 5, a sliding window process is used in the firstmodule 510 of the sensor sensitivity estimator 500 in one embodiment tomitigate between the desire for a real time decision process and thenecessity of redundancy for sensor current characteristics estimation.An example of the sliding window process is illustrated in accordancewith one embodiment of the present disclosure in FIG. 9.

For instance, in one aspect, during the processing performed by thefirst module 510, at each iteration of the decision process, a timewindow is selected, and based on the sensor current signals determinedduring the selected time window, one or more predetermined sensorcharacteristics are determined. By way of nonlimiting examples, the oneor more predetermined sensor characteristics may include the meancurrent signal level, the current signal variance, the average slope ofthe current signal, and the average sensor life (or the time elapsedsince the insertion or transcutaneous positioning of the analytesensor).

Thereafter, the selected time window is then slid by a fixed number ofminutes for the next iteration. In one aspect, the width or duration ofthe time window and the incremental step size may be predetermined orestablished to 60 minutes, thus generating non-overlapping time windowsto minimize potential correlation between decisions. Within the scope ofthe present disclosure, other approaches may be contemplated, forexample, where the sliding time windows may include time duration ofapproximately 30 minutes with an incremental one minute step.

In one aspect, the following expressions may be used to determine thesensor characteristic estimations discussed above such as, for example,the sensor signal mean, the average slope and the variance values:

$\begin{bmatrix}{mean} \\{slope}\end{bmatrix} = {( {X^{\prime}X} )^{- 1}X^{\prime}Y}$

where X is a matrix with a column of 1s and a column of data index and Yis a column vector of current values

${variance} = {\frac{1}{n - 1}{\sum\limits_{t = 0}^{width}\;( {{current}_{t + 1} - {mean}} )^{2}}}$

where t is the index of the first available data point in the timewindow.

Referring back to FIG. 5, after estimating or determining the sensorcharacteristics described above, a four-dimensional feature vectorcorresponding to a time window of sensor current signal is generated. Inone aspect, the generated feature vector and logistic regressionapproach may be used to estimate the probability that the sensor isundergoing or experiencing early signal attenuation (ESA) during each ofthe predetermined time window. In one aspect, the logistic regressionapproach for determination or estimation of the probability of ESApresence Pr[ESA] may be expressed as follows:

$\begin{matrix}{{{\Pr\lbrack {{ESA}❘x_{n}} \rbrack} = \frac{\exp^{\langle{\beta,x_{n}}\rangle}}{1 + \exp^{\langle{\beta,x_{n}}\rangle}}}{x_{n} = \begin{bmatrix}1 \\{\log( {mean}_{n} )} \\{\log( {variance}_{n} )} \\{slope}_{n} \\{\log( {sensorlife}_{n} )}\end{bmatrix}}} & (1)\end{matrix}$

In one aspect, the coefficient vector β plays a significant role in theefficiency of the sensor signal attenuation estimation. That is, in oneembodiment, a predetermined number of sensor insertions may be used toempirically determine or estimate the model coefficients. Morespecifically, in one aspect, a bootstrap estimation procedure may beperformed to add robustness to the model coefficients. For example, ageneralized linear model fit approach may be applied to a predeterminedtime period to determine the coefficient vector β. Based on a predefinednumber of iterations, an empirical probability distribution function ofeach coefficient may be determined, for example, as shown in FIG. 10,where each selected coefficient corresponds to the mode of theassociated distribution.

After the determination of the one or more sensor currentcharacteristics or parameters, and the determination of thecorresponding coefficients, the probability of ESA presence Pr[ESA] isestimated based on, in one embodiment, the following expression:

$\begin{matrix}{{\Pr\lbrack {{ESA}❘x_{n}} \rbrack} = \frac{\exp^{1.511 - {1.813 \times {\log{({mean})}}} + {0.158 \times {slope}} + {0.399 \times {\log{({variance})}}} - {0.576 \times {\log{({SensorLife})}}}}}{1 + \exp^{1.511 - {1.813 \times {\log{({mean})}}} + {0.158 \times {slope}} + {0.399 \times {\log{({variance})}}} - {0.576 \times {\log{({SensorLife})}}}}}} & (2)\end{matrix}$

It is to be noted that within the scope of the present disclosure, theestimation of the probability of the ESA presence Pr[ESA] as describedby the function shown above may be modified depending upon the design orthe associated underlying parameters, such as, for example, the time ofday information, or the detrended variance of the sensor current signal,among others.

Referring yet again to FIG. 5, after the determination of theprobability of ESA presence based on the estimation described above, inone aspect, the estimated probability is compared to a preselectedthreshold level, and based on the comparison, a request for capillaryblood glucose measurement may be prompted. In one aspect, thepredetermined threshold level may include 0.416 for comparison with theestimated probability of ESA presence. Alternatively, within the scopeof the present disclosure, the predetermined threshold level may varywithin the range of approximately 0.3 to 0.6.

As described above, in one aspect of the present disclosure, the firstmodule 510 of the sensor sensitivity estimator 500 (FIG. 5) isconfigured to perform estimation of the probability of ESA presencebased on the characteristics or parameters associated with the analytesensor and the sensor current signals. In one embodiment, the secondmodule 520 of the sensor sensitivity estimator 500 (FIG. 5) may beconfigured to perform additional processing based on capillary bloodglucose measurement to provide substantially real time estimation of theearly sensor attenuation (ESA) of the analyte sensors. That is, sinceESA is defined by a drop or decrease of sensitivity (that is, thecurrent signal of the sensor over the blood glucose ratio), thedistribution of the sensitivity during ESA occurrence is generally lowerthan the distribution during normal functioning conditions.Additionally, based on the non linear relationship between the sensorcurrent level and blood glucose measurements, the ESA presenceprobability estimation using capillary blood measurements may bedetermined using a bin (e.g., category) construction approach, as wellas the estimation of the empirical distribution functions of the nominalsensitivity ratio.

More particularly, in one aspect of the present disclosure, theinstantaneous sensitivity (IS) may be defined as the ratio of the actualcurrent value of the analyte sensor and the actual blood glucose valueat a given point in time (defined, for example, by the expression (a)below. However, due to noise in the signals, for example, particularlyin the case of a stand alone measurement such as a single blood glucosemeasurement, the instantaneous sensitivity (IS) may be approximated bydetermining the average sensor current signal levels around the time ofthe fingerstick blood glucose determination, for example, by theexpression (b) shown below.

$\begin{matrix}{{{a.\mspace{14mu}{DS}_{t}} = \frac{{current}_{t}}{{BG}_{t}}}{{b.\mspace{14mu}{IS}_{t}} = \frac{\frac{1}{11}{\sum\limits_{t = {- 5}}^{5}\;{current}_{t + 1}}}{{BG}_{t}}}} & (3)\end{matrix}$

Given that each analyte sensor has a different sensitivity, and thus theinstantaneous sensitivity (IS) is highly sensor dependent, the absolutevalue of the instantaneous sensitivity (IS) may not provide reliableindication of ESA presence. On the other hand, during manufacturing,each analyte sensor is associated with a nominal sensitivity value.Accordingly, the ratio of the instantaneous sensitivity over the sensornominal sensitivity will result in a more sensor independent, reliableESA detection mechanism. Accordingly, the sensitivity ratio Rs(t) attime t may be defined in one aspect as follows:

$\begin{matrix}{R_{S{(t)}} = {\frac{{IS}(t)}{S_{nominal}} = {\frac{1}{11}\frac{\sum\limits_{l = {- 5}}^{5}\;{current}_{t + 1}}{S_{nominal} \times {BG}_{t}}}}} & (4)\end{matrix}$

Referring to the discussion above, the blood glucose bin/categoryconstruction approach in one embodiment may include defining atransformation of the blood glucose measurement scale which rectifies adiscrepancy between the measured and estimated blood glucose values.That is, in one aspect, the defined transformation approach correspondsto or is associated with a typical distribution of blood glucose levels.For example, the transformation approach defining the variousbins/categories may be determined based on the following expression:

r=1.509×e ^(1.084×log(log(BG)-5.381)) where BG is in mg/dl  (5)

where the following scaled glucose bins may be defined:

1. r<−2, severe hypoglycemia

2. −2≤r<−1, mild hypoglycemia

3. −1≤r<0, low euglycemia

4. 0≤r<1, high euglycemia

5. 1≤r<2, mild hyperglycemia

6. 2≤r, severe hyperglycemia

Upon determination of the bin/category for use with the estimation ofthe probability of ESA presence, in one aspect, kernel densityestimation (using Gaussian kernel, 24, for example) may be used toestimate the distribution of the sensitivity ratio Rs in eachbin/category described above. In one aspect, this estimation of thedistribution in sensitivity ratio Rs is shown in FIG. 11, where for eachbin/category (including, for example, severe hypoglycemia (bin1), mildhypoglycemia (bin2), low euglycemia (bin3), high euglycemia (bin4), mildhyperglycemia (bin5), and high hyperglycemia (bin6)), each chartillustrates the associated distribution where ESA presence is detected.

Referring again to the discussions above, based on the estimation of theprobability density functions of the estimated distribution of thesensitivity ratio Rs in each bin/category, in one aspect, anon-parametric hypothesis testing approach based on Bayes' law may beimplemented. For example, in one aspect of the present disclosure, fromBayes' law, the estimated probability of ESA presence knowing thesensitivities ratio and the blood glucose bin/category may be decomposedbased on the following expression:

$\begin{matrix}{{\Pr\lbrack {{{ESA}❘R_{s}} = {{{\rho\&}\mspace{14mu} r} \in {bin}_{i}}} \rbrack} = \frac{\pi_{ESA}{{\hat{f}}_{i}(\rho)}}{{\pi_{ESA}{{\hat{f}}_{{ESA},i}(\rho)}} + {\pi_{\overset{\_}{ESA}}{{\hat{f}}_{\overset{\_}{ESA},i}(\rho)}}}} & (6)\end{matrix}$

where π_(a) is the proportion of events in class a and {circumflex over(f)}

is the previously estimated probability density function of R_(S) inbin/category i for class a.

In addition, to minimize the overall probability of error, the followingdecision rule may be applied:

$\begin{matrix}{ {{{sensor}\mspace{14mu}{is}\mspace{14mu}{ESA}\mspace{14mu}{if}\mspace{14mu}\frac{\Pr\lbrack {{{ESA}❘R_{s}} = {{{\rho\&}\mspace{14mu} r} \in {bin}_{i}}} \rbrack}{\Pr\lbrack {{\overset{\_}{ESA}❘R_{s}} = {{{\rho\&}\mspace{14mu} r} \in {bin}_{i}}} \rbrack}} > 1}\Rightarrow{\frac{{\hat{f}}_{{ESA},i}(\rho)}{{\hat{f}}_{\overset{\_}{ESA},i}(\rho)} > \frac{\pi_{\overset{\_}{ESA}}}{\pi_{ESA}}} {{{assuming}\mspace{14mu}\pi_{\overset{\_}{ESA}}} = {\pi_{ESA} =  0.5\Rightarrow{\frac{{\hat{f}}_{{ESA},i}(\rho)}{{\hat{F}}_{\overset{\_}{ESA},i}(\rho)} > 1}\Rightarrow{{{\hat{f}}_{{ESA},i}(\rho)} > {{\hat{f}}_{\overset{\_}{ESA},i}(\rho)}} }}} & (7)\end{matrix}$

Accordingly, based on the above, the hypothesis analysis module (521) ofthe second module 520 shown in FIG. 5 in one embodiment may beconfigured to verify/confirm the presence of ESA for a given analytesensor based on the capillary blood glucose level measurement reading,when the capillary blood glucose measurement is in bin/category i, whenthe sensitivity ratio Rs is less than the corresponding definedthreshold level t_(i). For example, given the six blood glucosebins/categories (bin1 to bin6) described above, the respective thresholdlevel t₁ is: t₁=1.138, t₂=0.853, t₃=0.783, t₄=0.784, t₅=0.829, andt₆=0.797.

In this manner, in one aspect of the present disclosure, the method,system and computer program product provides for, but not limited to,early detection of sensitivity drops in continuous glucose monitoringsystems. Sensitivity drops can be found in the first 24 hours, forexample, of the sensor life, and while the potential adverse impacts maybe minimized by frequent calibration or sensor masking, such sensitivitydrops have clinically significant effects on the accuracy of the sensordata, and in turn, potential danger to the patient using the sensor.Accordingly, in one aspect, there is provided method, system, andcomputer program product for estimating or determining the probabilityof the presence of ESA based on the sensor current signalcharacteristics, and thereafter, performing a confirmation orverification routine to determine whether the sensitivity dropprobability estimated based on the sensor current signal characteristicscorresponds to a real time occurrence of a corresponding sensitivitydrop in the sensor.

Accordingly, sensor accuracy, and in particular in the criticalhypoglycemic ranges may be improved, multiple calibrations and/or sensormasking may be avoided during the early stages of the sensor life, andfurther, sensor calibration during sensitivity drop occurrence which mayresult in undetected hypoglycemic events, may be avoided.

FIG. 6 is a flowchart illustrating an overall ESA detection routine inaccordance with one embodiment of the present disclosure. Referring toFIG. 6, in one embodiment of the present disclosure, a predeterminednumber of sensor data is retrieved or collected (610), and thereafter,it is determined whether the probability estimation for the sensitivitydecline determination is appropriate (620). In one aspect, one or moreof the following parameters may be used to determine whether thedetermination of the probability estimation of the sensitivity declineis appropriate: presence or collection of sufficient data pointsassociated with the analyte sensor, timing of the probability estimationrelative to when the analyte sensor was inserted or subcutaneouslypositioned, time period since the most recent determination of theprobability estimation for the sensitivity decline, among others.

If it is determined that the probability estimation for the sensitivitydecline determination is not appropriate (620), then the routine shownin FIG. 6 returns to collecting additional sensor data points. On theother hand, if it is determined that the probability estimation for thesensitivity decline determination is appropriate, then the probabilityestimation for the sensitivity decline determination is performed (630).Thereafter, based upon the determined probability estimation for thesensitivity decline, it is determined whether ESA is present or not(640).

That is, based on the analysis performed, for example, by the firstmodule 510 of the sensitivity decline detector 500 (FIG. 5), if ESA isnot detected, then the routine returns to collection and/or retrieval ofadditional sensor current data (610). On the other hand, if based on theanalysis described above ESA is detected (640), then a capillary bloodmeasurement is requested (for example, by prompting the user to performa fingerstick blood glucose test and input the blood glucose value)(650). Thereafter, the routine shown in FIG. 6 performs the routine forconfirming the presence or absence of ESA (660) by, for example, thehypothesis analysis module 521 (FIG. 5).

Referring again to FIG. 6, if based on the analysis using the capillaryblood measurement determines that ESA is not present (670), the routineagain returns to the data collection/retrieval mode (610). On the otherhand, if ESA presence is determined (670), in one aspect, an alarm ornotification may be generated and provided to the user (680) to alertthe user.

FIG. 7 is a flowchart illustrating real-time detection of sensor currentabnormalities described in conjunction with module 1 of FIG. 5 inaccordance with one embodiment of the present disclosure. Referring toFIG. 7, in one embodiment, analyte sensor data for a defined time periodis retrieved or selected. With the analyte sensor data, one or more dataprocessing is performed to determine sensor signal characteristics,including, for example, the mean current signal, the least squaresslope, a standard deviation, an average elapsed time since the analytesensor insertion/positioning (or average sensor life), a variance aboutthe least squares slope (710).

Referring to FIG. 7, predetermined coefficients based on the analytesensor data may be retrieved (720), and applied to the analyte sensorsignals to determine or estimate the probability of ESA presence (730).Additionally, further shown in FIG. 7 is a predetermined threshold (740)which in one embodiment may be compared to the determined estimatedprobability of ESA presence (750). In one aspect, the predeterminedthreshold may be determined as the minimum probability of ESA presencefor declaring such condition, and may be a tradeoff between false alarms(false positives, where the threshold may be easy to exceed) versusmissed detections (false negatives, where the threshold is difficult toexceed).

Referring still again to FIG. 7, if it is determined that the estimatedprobability of ESA presence does not exceed the predetermined threshold(750), then it is determined that ESA is not present—that is, sensorcurrent signal attenuation is not detected (760). On the other hand, ifit is determined that estimated probability of ESA presence exceeds thepredetermined threshold, it is determined that ESA is present—that is,sensor current signal attenuation is detected (770). In either case,where the ESA presence is determined to be present or not present, suchdetermination is communicated or provided to the subsequent stage in theanalysis (780) for further processing.

FIG. 8 is a flowchart illustrating verification routine of module 2 inFIG. 5 to confirm or reject the output of module 1 in accordance withone embodiment of the present disclosure. Referring to FIG. 8, with thecontinuous glucose data (801) and the capillary blood glucosemeasurement (802), an average function of one or more continuous glucosesensor current data point at around the same time or approximatelycontemporaneously with the blood glucose measurement is performed (803).In the case where the sensor current data point is a single value,average function will result in the value itself—therefore averagingroutine is unnecessary.

Alternatively, in the case where the sensor data includes more than onedata point, for example, 11 data points centered around the time of theblood glucose data point, the average function is performed resulting inan average value associated with the plurality of data points.Thereafter, as shown in FIG. 8, a sensitivity value (S) is determinedbased on the calculated average value of the sensor data points asdescribed above and the capillary blood glucose measurement (805). Forexample, the sensitivity value (S) associated with the sensor may bedetermined as the ratio of the determined average sensor data pointvalue to the blood glucose value.

Referring still to FIG. 8, a nominal sensor sensitivity typicallydetermined at the time of sensor manufacturing (807) is retrieved andapplied to the determined sensor sensitivity value (S) to attain anormalized sensitivity ratio Rs (808).

Referring back to FIG. 8, based on the measured or received capillaryblood glucose measurement (802), a corresponding glucose bin describedabove is determined or calculated (804), for example, in one aspect, byapplying the function described in equation (5) above. Thereafter, acorresponding ESA test threshold t is determined (806) based on thecalculated or determined glucose bin. For example, as described above,each glucose bin (bin1 to bin6), is associated with a respectivethreshold level t which may, in one aspect, be determined by prioranalysis or training.

Referring still again to FIG. 8, with the normalized sensitivity ratio(808) and the calculated bin (806), a comparison is made between thenormalized sensitivity ratio and the determined or calculated bin (809).For example, in the case where the comparison establishes the normalizedsensitivity ratio (Rs) exceeds the calculated bin t, it is determinedthat early signal attenuation (ESA) is not present (811). On the otherhand, when the normalized sensitivity ratio (Rs) is determined to beless than the calculated bin t, then it is determined that ESA in thesensor signals is present (810).

FIG. 9 illustrates a real time current signal characteristics evaluationapproach based on a sliding window process of module 1 in FIG. 5 inaccordance with one embodiment of the present disclosure.

FIG. 10 illustrates bootstrap estimation of coefficients for module 1 ofFIG. 5 in accordance with one embodiment of the present disclosure.Referring to the Figures, the bootstrap estimation procedure performedto add robustness to the model coefficients may include, in one aspect,a generalized linear model fit applied to a predetermined time period todetermine the coefficient vector β. Based on a predefined number ofiterations, an empirical probability distribution function of eachcoefficient may be determined, for example, as shown in FIG. 10, whereeach selected coefficient corresponds to the mode of the associateddistribution.

FIG. 11 illustrates Gaussian kernel estimation of the normalizedsensitivity density of module 2 of FIG. 5 in accordance with oneembodiment of the present disclosure. Referring to FIG. 11, as describedabove in conjunction with FIG. 5, in one aspect, kernel densityestimation (using Gaussian kernel, for example) may be used to estimatethe distribution of the sensitivity ratio Rs in each bin/categorydescribed above. The estimation of the distribution in sensitivity ratioRs in one aspect is shown in FIG. 11, where for each bin/category(including, for example, severe hypoglycemia (bin1), mild hypoglycemia(bin2), low euglycemia (bin3), high euglycemia (bin4), mildhyperglycemia (bins), and high hyperglycemia (bin6)), the correspondingchart illustrates the associated distribution where ESA presence isdetected as compared to the distribution where no ESA presence isdetected.

FIG. 12 illustrates a comparison of rate of false alarms (falsepositives) and sensitivity decline detection rate in accordance with theembodiment of the present disclosure. That is, FIG. 12 represents therelation between ESA detection rate and false alarm rate. In one aspect,curve 1210 illustrates the output results of the first module 510 in thesensitivity decline detector 500 (FIG. 5) based on the logisticregression classifier, while curve 1220 illustrates the combined outputof the first module 510 and the second module 520 of the sensitivitydecline detector 500 (FIG. 5) based, for example, on a logistic ruleclassifier prompting a blood glucose measurement in the case of ESApresence probability exceeding a predetermined threshold level. In oneembodiment, based on a threshold level of 0.416 determining the ESApresence probability, the rate of ESA detection is approximately 87.5%and a false alarm rate is approximately 6.5%.

In the manner described above, in accordance with the variousembodiments of the present disclosure, real time detection of ESA ornight time dropouts of analyte sensor sensitivities are provided. Forexample, an analyte sensor with lower than normal sensitivity may reportblood glucose values lower than the actual values, thus potentiallyunderestimating hyperglycemia, and triggering false hypoglycemia alarms.Moreover, since the relationship between the sensor current level andthe blood glucose level is estimated using a reference blood glucosevalue (for example, calibration points), if such calibration isperformed during a low sensitivity period, once the period comes to anend, all glucose measurements will be positively biased, thuspotentially masking hypoglycemia episodes. Accordingly, the occurrenceof errors in the relation between the current signal output of theanalyte sensor and the corresponding blood glucose level may bemonitored and detected in real time such that the patients may beprovided with the ability to take corrective actions.

Indeed, real time detection of variations in the glucose levels inpatients using monitoring devices such as analyte monitoring devicesprovide temporal dimension of glucose level fluctuations which providethe ability to tightly control glycemic variation to control diabeticconditions. More specifically, in accordance with the variousembodiments of the present disclosure, the analyte monitoring systemsmay be configured to provide warnings about low glucose levels in realtime in particular, when the patient may not be suspecting hypoglycemiaor impending hypoglycemia, and thus provide the ability to help patientsavoid life-threatening situations and self-treat during hypoglycemicattacks.

Accordingly, in one aspect of the present disclosure, the detection ofepisodes of low sensor sensitivity includes a first module which may beconfigured to execute a real-time detection algorithm based on analytesensor current signal characteristics, and further, a second modulewhich may be configured to perform a statistical analysis based on asingle blood glucose measurement to confirm or reject the initialdetection of the sensor sensitivity decline performed by the firstmodule. In this manner, in one aspect of the present disclosure,accurate detection of ESA episodes or night time dropouts or declines insensor current signal levels may be provided with minimal false alarms.

Accordingly, a computer implemented method in one aspect includesreceiving a plurality of analyte sensor related signals, determining aprobability of signal attenuation associated with the received pluralityof analyte sensor related signals, verifying the presence of signalattenuation when the determined probability exceeds a predeterminedthreshold level, and generating a first output signal associated withthe verification of the presence of signal attenuation.

Further, determining the probability of signal attenuation may includedetermining one or more characteristics associated with the receivedplurality of analyte sensor related signals, and applying apredetermined coefficient to the plurality of analyte sensor relatedsignals.

In another aspect, the determined one or more characteristics mayinclude one or more mean value associated with the analyte sensorrelated signals, the least square slope associated with the analytesensor related signals, a standard deviation associated with the analytesensor related signals, an average elapsed time from positioning theanalyte sensor, or a variance about a least squares slope associatedwith the analyte sensor related signals.

Also, in still another aspect, the predetermined threshold level may beuser defined or defined by a system expert.

In still another aspect, when the determined probability does not exceedthe predetermined threshold level, the method may further includegenerating a second output signal associated with absence of signalattenuation condition.

Additionally, in yet a further aspect, verifying the presence of signalattenuation may include selecting a signal attenuation threshold level,determining a sensitivity level associated with the analyte relatedsensor signals, and confirming the presence of signal attenuation basedat least in part on a comparison of the determined sensitivity level andthe selected signal attenuation threshold level, where the signalattenuation threshold level may be associated with a blood glucosemeasurement.

Also, the blood glucose measurement may in another aspect include acapillary blood glucose sampling.

In yet still another aspect, the sensitivity level associated with theanalyte related sensor may include a ratio of nominal sensitivityassociated with the analyte related sensor signals and the sensitivityvalue associated with the analyte related sensor signals, where thesensitivity value may be determined as a ratio of the average of theanalyte related sensor signals and a blood glucose measurement.

Moreover, confirming the presence of signal attenuation in anotheraspect may include determining that the sensitivity level is less thanthe selected signal attenuation threshold level, which in one aspect,may be determined by a system expert.

An apparatus in accordance with another aspect of the present disclosureincludes a data storage unit, and a processing unit operatively coupledto the data storage unit configured to receive a plurality of analytesensor related signals, determine a probability of signal attenuationassociated with the received plurality of analyte sensor relatedsignals, verify the presence of signal attenuation when the determinedprobability exceeds a predetermined threshold level, and generate afirst output signal associated with the verification of the presence ofsignal attenuation.

The processing unit may be configured to determine the probability ofsignal attenuation and is configured to determine one or morecharacteristics associated with the received plurality of analyte sensorrelated signals, and to apply a predetermined coefficient to theplurality of analyte sensor related signals.

The determined one or more characteristics may include one or more meanvalue associated with the analyte sensor related signals, the leastsquare slope associated with the analyte sensor related signals, astandard deviation associated with the analyte sensor related signals,an average elapsed time from positioning the analyte sensor, or avariance about a least squares slope associated with the analyte sensorrelated signals, where the predetermined threshold level may be userdefined, or defined by a system expert.

When the determined probability does not exceed the predeterminedthreshold level, the processing unit may be further configured togenerate a second output signal associated with absence of signalattenuation condition.

In still another aspect, the processing unit may be further configuredto select a signal attenuation threshold level, determine a sensitivitylevel associated with the analyte related sensor signals, and confirmthe presence of signal attenuation based at least in part on acomparison of the determined sensitivity level and the selected signalattenuation threshold level.

The signal attenuation threshold level may be associated with a bloodglucose measurement.

The blood glucose measurement may include a capillary blood glucosesampling.

The sensitivity level associated with the analyte related sensor mayinclude a ratio of nominal sensitivity associated with the analyterelated sensor signals and the sensitivity value associated with theanalyte related sensor signals, where the sensitivity value may bedetermined as a ratio of the average of the analyte related sensorsignals and a blood glucose measurement.

The processing unit may be further configured to determine that thesensitivity level is less than the selected signal attenuation thresholdlevel, which may be, in one aspect determined by a system expert.

In still another aspect, the apparatus may include a user output unitoperatively coupled to the processing unit to display the first outputsignal.

A system for detecting signal attenuation in a glucose sensor in stillanother aspect of the present disclosure includes an analyte sensor fortranscutaneous positioning through a skin layer of a subject, a dataprocessing device operatively coupled to the analyte sensor toperiodically receive a signal associated with the analyte sensor, thedata processing device configured to determine a probability of theearly signal attenuation (ESA), and to verify the presence of earlysignal attenuation based on one or more predetermined criteria.

The data processing device may include a user interface to output one ormore signals associated with the presence or absence of early signalattenuation associated with the analyte sensor.

Various other modifications and alterations in the structure and methodof operation of this disclosure will be apparent to those skilled in theart without departing from the scope and spirit of the disclosure.Although the disclosure has been described in connection with specificpreferred embodiments, it should be understood that the disclosure asclaimed should not be unduly limited to such specific embodiments. It isintended that the following claims define the scope of the presentdisclosure and that structures and methods within the scope of theseclaims and their equivalents be covered thereby.

1-25. (canceled)
 26. A computer implemented method, comprising:receiving a set of analyte sensor data taken over a first time periodafter initialization of a sensor; performing a sliding window analysison the set of analyte sensor data, wherein performing the sliding windowanalysis comprises extracting a first sensor data characteristic for afirst window of the set of analyte sensor data, wherein the first windowstarts at a first start time, ends at a first end time, and has a firstduration less than the first time period; determining a probability ofexistence of signal attenuation associated with a decline in analytesensor response based on the first sensor data characteristics; andcomparing the determined probability of existence of signal attenuationto a predetermined threshold value.
 27. The computer implemented methodof claim 26, further comprising the steps of: extracting a second sensordata characteristic for a second window of the set of analyte sensordata, wherein the second window starts at a second start time after thefirst start time, ends at a second end time after the first end time,and has a second duration less than the first time period; determining asecond probability of existence of signal attenuation associated with adecline in analyte sensor response based on the first and second sensordata characteristics; and comparing the determined second probability ofexistence of signal attenuation to a predetermined threshold value. 28.The computer implemented method of claim 26, wherein the first sensordata characteristic is a mean value or a variance value.
 29. Thecomputer implemented method of claim 26, wherein the first sensor datacharacteristic is an average slope of a sensor signal.
 30. The computerimplemented method of claim 26, wherein the first sensor datacharacteristic is an average sensor life or a time elapsed sinceinsertion of the sensor.
 31. The computer implemented method of claim26, wherein the first time period includes a time in the first twelve totwenty four hours after insertion of the sensor.
 32. The computerimplemented method of claim 26, wherein the analyte sensor data isglucose sensor data.
 33. The computer implemented method of claim 26,wherein the analyte sensor data is lactate sensor data.
 34. The computerimplemented method of claim 26, wherein the analyte sensor data is aketone sensor data.
 35. The computer implemented method of claim 26,further comprising the step of computing an estimated decline in analytesensor response.
 36. An apparatus, comprising: a data storage unit; anda processing unit operatively coupled to the data storage unit, theprocessing unit programmed to: perform a sliding window analysis on aset of analyte sensor data, wherein the set of analyte sensor data istaken over a first time period after initialization of a sensor, andwherein in performance of the sliding window analysis, the processingunit is programmed to extract a first sensor data characteristic for afirst window of the set of analyte sensor data, wherein the first windowstarts at a first start time, ends at a first end time, and has a firstduration less than the first time period; determine a probability ofexistence of signal attenuation associated with a decline in analytesensor response based on the first sensor data characteristics; andcompare the determined probability of existence of signal attenuation toa predetermined threshold value.
 37. The apparatus of claim 36, whereinthe processing unit is further programmed to: extract a second sensordata characteristic for a second window of the set of analyte sensordata, wherein the second window starts at a second start time after thefirst start time, ends at a second end time after the first end time,and has a second duration less than the first time period; determine asecond probability of existence of signal attenuation associated with adecline in analyte sensor response based on the first and second sensordata characteristics; and compare the determined second probability ofexistence of signal attenuation to a predetermined threshold value. 38.The apparatus of claim 36, wherein the first sensor data characteristicis a mean value or a variance value.
 39. The apparatus of claim 36,wherein the first sensor data characteristic is an average slope of asensor signal.
 40. The apparatus of claim 36, wherein the first sensordata characteristic is an average sensor life or a time elapsed sinceinsertion of the sensor.
 41. The apparatus of claim 36, wherein thefirst time period includes a time in the first twelve to twenty fourhours after insertion of the sensor.
 42. The apparatus of claim 36,wherein the analyte sensor data is glucose sensor data.
 43. Theapparatus of claim 36, wherein the analyte sensor data is lactate sensordata.
 44. The apparatus of claim 36, wherein the analyte sensor data isa ketone sensor data.
 45. The apparatus of claim 36, wherein theprocessing unit is further programmed to compute an estimated decline inanalyte sensor response.